exportDataset: Export a dataset to a file

In crunch: Crunch.io Data Tools

Export a dataset to a file

Description

This function allows you to write a CrunchDataset to a .csv or SPSS .sav file.

Usage

exportDataset(
  dataset,
  file,
  format = c("csv", "spss", "parquet"),
  categorical = c("name", "id"),
  na = NULL,
  varlabel = c("name", "description"),
  include.hidden = FALSE,
  ...
)

## S4 method for signature 'CrunchDataset'
write.csv(x, ...)

Arguments

dataset	CrunchDataset, which may have been subsetted with a filter expression on the rows and a selection of variables on the columns.
file	character local filename to write to
format	character export format: currently supported values are "csv" and "spss" (and experimental support for "parquet").
categorical	character: export categorical values to CSV as category "name" (default) or "id". Ignored by the SPSS exporter.
na	Similar to the argument in utils::write.table(), 'na' lets you control how missing values are written into the CSV file. Supported values are: NULL, the default, which means that categorical variables will have the category name or id as the value, and numeric, text, and datetime variables will have the missing reason string; A string to use for missing values. "" means that empty cells will be written for missing values for all types.
varlabel	For SPSS export, which Crunch metadata field should be used as variable labels? Default is "name", but "description" is another valid value.
include.hidden	logical: should hidden variables be included? (default: FALSE)
...	additional options. See the API documentation. Currently supported boolean options include 'include_personal' for personal variables (default: FALSE) and 'prefix_subvariables' for SPSS format: whether to include the array variable's name in each of its subvariables "varlabels" (default: FALSE).
x	(for write.csv) CrunchDataset, which may have been subsetted with a filter expression on the rows and a selection of variables on the columns.

Value

Invisibly, file.

Examples

## Not run: 
csv_file <- exportDataset(ds, "data.csv")
data <- read.csv(csv_file)

# parquet will likely read more quickly and be a smaller download size
parquet_file <- exportDataset(ds, "data.parquet")
# data <- arrow::read_parquet(parquet_file) # The arrow package can read parquet files

## End(Not run)

crunch documentation built on May 29, 2024, 5:03 a.m.